The invention relates to a system for advising or averting potentially dangerous driving situations based on an analysis of driver stress resulting from not only current environmental conditions and current stress levels but also past environmental conditions and previous reactions and stress levels of the driver.
Body sensors and dedicated software packages exist which are able to extract feature vectors during an automobile trip and machine learning techniques have been applied to estimate the driver stress with a target input was derived from a separate inquiry. The capability exist for a system to be installed in an automobile which infers driver stress level from body sensors (blood pressure, ECGEMG, galvanic skin response, respiration, gaze direction, use of pedal and wheel controls etc.). Furthermore, automobiles have digital maps which can be used by an on bullet navigation system for these digital maps maybe enhanced with a variety of attributes, such as traffic control, signs, detail lane intersection structure, traffic flow, etc.
A driver""s stress level depends on a variety of determining factors, some of which are beyond the scope of the system (personal situations at home or at work). However, other determining factors correlate to road and traffic conditions which can be retrieved from a digital map (for example, complex intersections, on and off ramps or left turns.) A detailed analysis of such determination of stress level is discussed in xe2x80x9cA Route advice Agent that Models Driver Preferencesxe2x80x9d, Seth Rogers, Claude-Nicholas Fiechter, Pat Langley, Third International Conference on Autonomous Agents, Seattle Wash. (1999).
Furthermore, current and future information and entertainment services such as cellular phones, e-mail or news reading applications and radio and navigation system messages require the driver""s attention and thus contribute additionally to the stress level.
Although prediction of stress level from body measurement can be accomplished fairly reliably as discussed in xe2x80x9cSmart Card: Detecting Driver Stressxe2x80x9d, Jennifer Healey, Rosalind Pichard, Proceedings ICPR 2000, Barcelona, Spain (2000), the various reactions of drivers under the same external conditions exhibit wide differences.
It is an object of the present invention to provide an individual module which allows for the adaptation of a system on an individual basis.
According to the present invention, a system learns to predict individual driver stress level from features given in a geographical database and from features which indicate current traffic situations. The geographic database provides features such as the current road condition, the type of road (highway, city, etc.), the type of intersection, traffic signs, speed limits, curvature information, and number of lanes. The current traffic situation can be determined by sensors already installed in automobiles for other purposes which can be used to infer features of the current traffic situation. Radar, ESP (Electronic Stability Program), ABS (Antilock Braking System), steering wheel angle, etc. provide information whereby a current traffic situation can be inferred. The outputs of these sensors can be synergistically combined. As an example, with an estimate of a current position with respect to the lanes of the highway obtained from the geographic database, and with optional information concerning turn signals, the system is able to detect lane changes and correlate driving maneuvers with individual stress level. The knowledge as to which lane is occupied by an automobile can be valuable in access the stress potential.
According to the present invention a system is developed which uses previously known machine learning techniques such as neural nets, radial basis functions, etc. to derive a model of the individual driver stress reaction. Subsequently, a learning phase can take place either continually or it can be limited to a dedicated training period. The resulting personal driving model can subsequently be used to predict stress levels in advance by, for example, taking into account approaching intersections, on-ramps, and off-ramps. The system is also able to take advantage of the knowledge of an upcoming route if a driver is following directions from the navigation system.
It is also possible to distinguish between traffic induced stress and a base level stress related to the current personal situation through the continual monitoring of driver stress over a period of time.
As a result of this system, a wide range of possible safety and convenience applications are currently possible. In the future, this system will be able to take advantage of additional information and entertainment services which are related or unrelated to the task of driving. As an example, in addition to the present use of radios and cellular phones, there will be e-mail and news reader services which all require additional attention away from the driving of a vehicle. Therefore, whether these services are used or they are seeking the attention of the driver as by ringing or other indication, they have the ability to contribute to the stress level which can lead to further driver distraction and potential safety risks.
Another aspect of the present invention is the use of current and predicted driver stress levels as an input for a service manager component which will enable or disable services. This allows enhanced information to be provided to the driver only when it is safely possible and the prediction of the stress level of upcoming situations is a determining factor as to whether a driver will be able to handle the diversions caused by these additional services. As an example, services could be either switched on, switched off, or modified, depending on the driver situation. As an example, a cellular phone call could be put on hold while a driver enters an on-ramp.
Another aspect of driver stress which is addressed by the present invention concerns the individualized reaction to particular driving conditions. That is, individual responses to further distractions concerning cellular phones etc. differ from person to person. It is the training aspect of the present invention which allows for direct tracking of the change in stress level after an incoming phone call so that the system can learn the impact caused by such services on the driver in order to improve a safe servicing schedule. In order to accomplish this training, machine learning techniques are applied to associate stress level with attributes of service (e.g. caller ID on cellular phone, category of news) and content-related features (the stock quotes). As a further example, features of the driver""s electronic calendar can be input as, for example, a meeting with the boss in the near future which could increase the driver stress level.
With this input and an estimate of the attention level required as well as the duration of various available services, the service manager component of the present invention can not only switch services on or off but control those services to a more sophisticated level (e.g. read e-mail based on priority).
With the present invention, automobile manufacturers and drivers will be able to comply with future safety restrictions or prohibitions on the use of cellular phones in cars. Imposed restrictions will be even more severe for services beyond cellular phones. That is, internet and e-commerce services can be expected to have severe restrictions placed on their use during driving. The present invention may be contemplated as a precondition for delivering additional services to drivers and yet still have all the convenience capabilities within the automobile.
Other objects, advantages and novel features of the present invention will become apparent from the following detailed description of the invention when considered in conjunction with the accompanying drawings.